Verification of an analytical system model for predicting dynamics of cereal pest populations

The object of the study is a neural network model for forecasting the dynamics of the number of grain pests. The main attention is paid to the verification of the artificial neural network model, on the basis of which the analytical system was developed. The paper solves the problem of developing a comprehensive method for verifying artificial neural network models. The proposed method combines the analysis of invariant sets, probabilistic assessment of stability, scenario testing and statistical assessment of the accuracy of the model. Unlike traditional methods based on calculations of the mean square error, the developed approach allows for a formal assessment of the probability of forecasts going beyond acceptable limits and analyzes the influence of external factors, such as temperature and humidity, on the accuracy of the forecast. Experimental validation was carried out using data on grain pest populations in the Republic of Kazakhstan from 2005 to 2022. The transformer-based prognostic model was trained on climatic and agronomic data. Model verification showed that the probability that the predictions will remain within the invariant set was 95.2%, with MSE 239.1234, determination coefficient R2 = 0.85. Scenario analysis showed that the model remained stable in the temperature range [15°C, 30°C], but the accuracy decreased under extreme conditions. The results indicate that the proposed complex method provides a more accurate assessment of the stability of forecasting compared to traditional approaches. The results can serve as a basis for further development of similar forecasting systems that can be used in agromonitoring and planning of crop protection measures